Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (4): 1093-1099.DOI: 10.11772/j.issn.1001-9081.2017.04.1093

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Cross-population differential evolution algorithm based on opposition-based learning

ZHANG Bin, LI Yanhui, GUO Hao   

  1. School of Information Management, Central China Normal University, Wuhan Hubei 430079, China
  • Received:2016-08-02 Revised:2016-09-28 Online:2017-04-10 Published:2017-04-19
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (71471073, 71171093), the Fundamental Research Funds for the Central Universities (CCNU14Z02016).


张斌, 李延晖, 郭昊   

  1. 华中师范大学 信息管理学院, 武汉 430079
  • 通讯作者: 郭昊
  • 作者简介:张斌(1993-),男,湖北襄阳人,硕士研究生,主要研究方向:物流系统优化、智能计算;李延晖(1974-),男,湖南衡阳人,教授,博士,主要研究方向:物流与供应链管理、管理信息系统;郭昊(1987-),男,湖北荆州人,博士研究生,主要研究方向:物流系统优化。
  • 基金资助:

Abstract: Aiming at the deficiencies of traditional Differential Evolution (DE) algorithm, low optimization accuracy and low convergence speed, a Cross-Population Differential Evolution algorithm based on Opposition-based Learning (OLCPDE) was proposed by using chaos dispersion strategy, opposition-based optimization strategy and multigroup parallel mechanism. The chaos dispersion strategy was used to generate the initial population, then the population was divided into sub-groups of the elite and the general, and a standard differential evolution strategy and a differential evolution strategy of Opposition-Based Learning (OBL) were applied to the two sub-groups respectively. Meanwhile, a cross-population differential evolution strategy was applied to further improve the accuracy and enhance population diversity for unimodal function. The sub-groups were handled through these three strategies to achieve co-evolution. After the experiments are totally run for 30 times independently, it is proven that the proposed algorithm can stably converge to the global optimal solution in 11 functions among 12 standard test functions, which is superior to other comparison algorithms. The results indicate that the proposed algorithm not only has high convergence precision but also effectively avoid trapping in local optimum.

Key words: Differential Evolution (DE), Opposition-Based Learning (OBL), cross-population, chaos search, function optimization

摘要: 针对差分进化(DE)算法存在的寻优精度低、收敛速度慢等问题,借鉴混沌分散策略、反向学习策略(OBL)以及跨种群并行机制,提出一种基于反向学习的跨种群差分进化算法(OLCPDE)。采用混沌分散策略进行种群初始化,将种群划分为精英种群和普通种群,对两个子种群分别采用标准的差分进化策略和基于反向学习的差分进化策略;同时,为进一步提高算法对单峰函数的求解精度和稳定性,采用了一种跨种群的差分进化策略,运用三种策略对子种群进行操作,达到共同进化的目的。实验独立运行30次,OLCPDE在12个标准的测试函数中,有11个函数都能稳定地收敛到全局最优解,优于对比算法。实验结果表明,OLCPDE收敛精度高,能有效避免陷入局部最优点。

关键词: 差分进化, 反向学习, 跨种群, 混沌搜索, 函数优化

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